Fund allocations and strategies are commonly based on investors’ self described investment philosophies (e.g., conservative, aggressive), combined with basic demographic data. These strategies rely on a limited number of data points, resulting in sub-optimal fund allocation.
Goals and Objectives
The goal of this use case is to provide highly custom advice and investment product offerings that are driven by individualized needs and behaviors, determined by predictive event analytics. This will improve net-new money inflows and increase share of wallet for financial institutions.
Predictive algorithms, AI/ML, Big data and analytics to capture social and behavioral client data (e.g., through social media) and integrate into investment profiling, New risk scoring models
Use Case Summary
Fund and wealth managers are able to offer ultra-personalized investment products, based on prediction of future events and behaviors determined by data analytics.